Architecture of a Database System by Joseph M. Hellerstein, Michael Stonebraker, James Hamilton

By Joseph M. Hellerstein, Michael Stonebraker, James Hamilton

Database administration platforms (DBMSs) are a ubiquitous and significant element of glossy computing, and the results of many years of analysis and improvement in either academia and undefined. structure of a Database method provides an architectural dialogue of DBMS layout ideas, together with method types, parallel structure, garage approach layout, transaction method implementation, question processor and optimizer architectures, and common shared elements and utilities. profitable advertisement and open-source structures are used as issues of reference, fairly while a number of replacement designs were followed by means of diversified teams. traditionally, DBMSs have been one of the earliest multi-user server platforms to be constructed, and therefore pioneered many structures layout strategies for scalability and reliability now in use in lots of different contexts. whereas a number of the algorithms and abstractions utilized by a DBMS are textbook fabric, structure of a Database method addresses the platforms layout matters that make a DBMS paintings. structure of a Database procedure is a useful reference for database researchers and practitioners and for these in different parts of computing drawn to the structures layout recommendations for scalability and reliability that originated in DBMS study and improvement.

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To keep that complexity bounded, most optimizers operate on individual SELECTFROM-WHERE query blocks in isolation and do not optimize across blocks. So rather than further complicate query optimizers, many systems rewrite queries into a form better suited for the optimizer. This transformation is sometimes called query normalization. One example class of normalizations is to rewrite semantically equivalent queries into a canonical form, in an effort to ensure that semantically equivalent queries will be optimized to produce the same query plan.

This is useful in scenarios where data availability is more important than completeness of results. But best-effort results do not have well-defined semantics, and for many workloads this is not a useful choice — particularly because the DBMS is often used as the “repository of record” in a multi-tier system, and availability-vs-consistency trade-offs tend to get done in a higher tier (often in an application server). The third approach is to employ redundancy schemes ranging from full database failover (requiring double the number of machines and software licenses) to fine-grain redundancy like chained declustering [43].

Each tuple in the database is assigned to an individual machine, and hence each table is sliced “horizontally” and spread across the machines. Typical data partitioning schemes include hash-based partitioning by tuple attribute, range-based partitioning by tuple attribute, round-robin, and hybrid which is a combination of both range-based and hash-based. Each individual machine is responsible for the access, locking and logging of the data on its local disks. During query execution, the query optimizer chooses how to horizontally re-partition tables and intermediate results across the machines to satisfy the query, and it assigns each machine a logical partition of the work.

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